Arroser, un premier pas... pour sortir de l’insécurité alimentaire
Bibliographic record
Abstract
Arroser permet de pallier l'absence prolongée de précipitations et donc d'améliorer la productivité d'une exploitation agricole. Cependant, en cas d'inondation et de destruction des récoltes, les pertes sont plus importantes à cause des coûts associés à l'arrosage. Nous établissons ici des critères pour aider les exploitants à faire face au mieux à l a variabilité des conditions clim atiques. La mise en place d'un coûteux système d'arrosage n'est pas toujours rentable. Par contre, arroser – même manuelle ment – une partie des parcelles plantées constitue toujours une bonne assurance contre le risque de sécheresse. Dans certaines conditions, l'arrosage permet non seulement d'augmenter le rendement espéré, mais également de diminuer la variabilité des revenus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".